1. Field of the Invention
The invention relates to a method of system performance tracking by providing a computer model (simulation) of a system having measurement sensors whereby the model can track, or follow, changes which occur in the performance of the system in real-time; these models are sometimes referred to as real-time or tracking models. The invention also relates to a method of tracking a system. It is in particular concerned with providing a tracking model of a gas turbine engine which can track the degradation and performance changes thereof, and although this specification refers mainly to tracking the performance of gas turbine engines, it should be understood that such a tracking model may be provided for any system which incorporates measurement sensors, and is not exclusively applicable to gas turbine engines. Such tracking models have practical use as tools in obtaining optimum performance of the engines, whereby varying control regimes can be applied to the model such that the control regime which provides optimum performance is determined. The optimum control regime can then be applied to the actual engine itself. In other words, depending on the state of the engine in terms of its performance (degradation), the optimum control regime for pre-set operating demand criteria will vary; if a model is provided which tracks the actual system then it may be used to obtain the optimum control regime as performance changes. Such tracking models also have application in engine health diagnosis and maintenance by allowing the performance of components of the engine to be assessed.
2. Discussion of Prior Art
Conventional engine controllers are designed on the assumption that all engines of a given type are represented by a `fixed` or `standard` engine. Often a fixed computer model of this standard engine is used to determine a control regime which achieves a number of guaranteed performance criteria. The controller is therefore designed for this fixed model, whose performance characteristics are assumed not to vary with time.
The performance of every engine however, is different because of, for example, build differences and tolerance variations in each individual engine. Additionally, as an engine ages, its performance degrades causing performance measures such as specific fuel consumption to decline. Engine deterioration through wear and damage therefore causes each engine to change in a time varying sense. Another source of time-dependant performance change is heat soakage (rematching of the engine due to thermal change of blade tip and seal clearances) which affects compressor and turbine efficiencies (these latter effects are reversible). Inevitably therefore, a number of compromises have to be made when designing controllers for a fixed model of gas turbine engine. Although the modelling differences between the actual engine and the fixed model tend to be small, they are significant; these small difference will lead to significant losses, e.g. in fuel consumption, when used to determine optimum control; corresponding gains can therefore be made if optimum control is obtained for the individual engine.
It is therefore advantageous if a suitable engine controller can use information pertaining to engine variation during the engine's operating life to obtain optimum performance levels by choice of suitable engine control data. It is estimated that control optimisation using a varying model, depending on applied demands, would enable a benefit in e.g. specific fuel consumption in the order of a 0.5-1% reduction, and a 17.degree. C. benefit in reduction of hot end temperature. Gains of this scale would be costly and hard won through other developmental approaches such as improved turbomachinery design. In fact, additional control and heat management system complexity for improvements in specific fuel consumption as small as 0.1%, are not uncommon on large civil aero-engines.
As far as optimisation of performance is concerned, the conventional control mode in which the engine is operating is normally fixed and represents a compromise between economic operation, performance and engine life. For example, for an aircraft when cruising at altitude it is known to be desirable to reduce either fuel burn, for economy, or turbine temperature so as to conserve engine life; but the inflexibility of conventional controllers will inhibit this.
It is known that models which track actual engine performance are useful in providing an optimising control strategy. Such systems are described in the paper "Subsonic Flight Test Evaluation of a Propulsion System Parameter Estimation Process for the F100 Engine" by J S Orme et al, published by the American Institute of Aeronautics and Astronautics AIAA-92-3745, and in NASA technical memorandum 104233 "A Simulation Study of Turbofan Engine Deterioration Estimation Techniques Using Kalman Filtering Techniques" by H. H. Lambert. The optimisation described therein is performed on a computer model of the engine and not on the engine itself. The aim of these tracking models is to use changes in sensor readings obtained from the engine at particular operating points to estimate changes in engine component performance; i.e. to calculate so called "deterioration parameters", which are also alternatively and hereinafter referred to as "performance parameters". These such parameters are, for example, efficiencies or flow capacities of turbines or compressors. Changes in performance parameters when incorporated into a computer model take the form of correction terms which when input to such models should result in computation of identical model sensor output changes as those readings from the actual engine at a particular operating point. When this is achieved the model is said to match or track the engine successfully. The model is usually a real-time thermodynamic model of the engine which typically, in addition to the normnal parameters such as fuel, guide vane and nozzle actuator positions, incorporates a further set of variables which represent these performance parameter changes.
The choice of the engine sensor outputs which the model uses to track is very important, they should give a broad measure of the condition of the engine so that when both sets of engine and model outputs are equal there is a reasonable level of confidence that the model is a good representation of the engine. This means that the sensors used must be widely distributed in terms of their independence.
Tracked models can be exploited practically to obtain control data which will maintain optimal efficiency for a certain required performance e.g. specific fuel consumption. In addition, a knowledge of the change in performance parameters is useful in monitoring degradation of components and to investigate suitable maintenance action.
The success of the model used for optimisation is crucially dependent on how well the model matches the engine since, as mentioned, the performance of every engine is different because of manufacturing tolerances, and will in any case deteriorate throughout the engine's operational life. Engine performance parameters are not directly measurable with engine control instrumentation but changes in their value can be estimated using prior knowledge of how such changes thereof affect changes in engine sensor outputs at a particular operating point. In mathematical terms, variations in the set (or vector) of engine performance parameters which may occur (hereinafter denoted as dP, gives rise to changes in the set (or vector) of engine sensor outputs (hereinafter denoted as dx), when control demand inputs to the engine are constant, i.e. at a particular operating condition. It is assumed that dP and dx are related linearly at a given engine operating point by a matrix of sensitivity coefficients C, such that:
dx=CdP PA1 dx contains m elements, m being the number of sensors used by the model, dP contains n elements, n being the number of performance parameters under consideration, C is a matrix of real numbers with m rows and n columns. PA1 a) providing a real time model having input from one or more of said sensors; PA1 b) storing at least one non-square coefficient matrix C relating sensor changes to performance parameter changes; and PA1 c) calculating a pseudoinverse of matrix C, where dx=C dP, where dx is the vector of system sensor changes and dP is the vector of performance parameter changes. PA1 b) from step (c) determining changes in performance parameters.
NASA Technical Memorandum 104233 describes turbofan engine deterioration estimation by providing a tracking model of the engine which uses Kalman filtering techniques to determine dP from dx. There is a significant problem with this technique in that the tracking can only be performed if there are as many sensors used as performance parameters to be estimated i.e. if m=n. This forces various assumptions to be made, and results in the tracking not being exact. It is often the case that the number of performance parameters liable to measurable variation exceeds the number of sensors. A given set of changes in the sensor outputs of the engine at a particular operating condition could be accounted for by a variety of different performance parameter changes.